In general image segmentation methods, for an image with a particular number of dimensions, segmentation and verification are performed using visualization information of only the particular number of dimensions. For example, when a two-dimensional image is segmented, visualization information of only the two-dimensional image is used. When a three-dimensional image is segmented, visualization information of only the third-dimensional image is used. That is, according to conventional image segmentation methods, although the geometrical characteristics of a segmented region is used for segmentation of a two dimensional image, visualization information of a three-dimensional image is not used for verification of the segmented two-dimensional image. Moreover, according to the conventional image segmentation methods, the result of segmentation using visualization information of a three-dimensional image is not applied to segmentation of a two-dimensional image. Accordingly, the conventional image segmentation methods are disadvantageous in that a user interface is restrictively provided.
In addition, according to the conventional image segmentation methods, segmentation tools by segmentation types and a drawing tool for modification operate independently, so segmentation and modification are performed inefficiently. In other words, operating environments must be separately set for the individual segmentation tools and drawing tool. Since the segmentation and modification results exist independently, a segmentation method using each segmentation tool and the segmentation result are not organically coordinated with each other, so synergy effects cannot be created.